OpenRaiser/Intern-Atlas huggingface dataset:
Technical Risks of (Lethal) Autonomous Weapons Systems
Venue: arXiv cs.CY 2025
Year: 2025

The autonomy and adaptability of (Lethal) Autonomous Weapons Systems, (L)AWS in short, promise unprecedented operational capabilities, but they also introduce profound risks that challenge the principles of control, accountability, and stability in international security. This report outlines the key technological risks associated with (L)AWS deployment, emphasizing their unpredictability, lack of transparency, and operational unreliability, which can lead to severe unintended consequences. Key Takeaways: 1. Proposed advantages of (L)AWS can only be achieved through objectification and classification, but a range of systematic risks limit the reliability and predictability of classifying algorithms. 2. These systematic risks include the black-box nature of AI decision-making, susceptibility to reward hacking, goal misgeneralization and potential for emergent behaviors that escape human control. 3. (L)AWS could act in ways that are not just unexpected but also uncontrollable, undermining mission objectives and potentially escalating conflicts. 4. Even rigorously tested systems may behave unpredictably and harmfully in real-world conditions, jeopardizing both strategic stability and humanitarian principles.
A Powerful Random Forest Featuring Linear Extensions (RaFFLE)
Venue: arXiv cs.LG 2025
Year: 2025

Random forests are widely used in regression. However, the decision trees used as base learners are poor approximators of linear relationships. To address this limitation we propose RaFFLE (Random Forest Featuring Linear Extensions), a novel framework that integrates the recently developed PILOT trees (Piecewise Linear Organic Trees) as base learners within a random forest ensemble. PILOT trees combine the computational efficiency of traditional decision trees with the flexibility of linear model trees. To ensure sufficient diversity of the individual trees, we introduce an adjustable regularization parameter and use node-level feature sampling. These modifications improve the accuracy of the forest. We establish theoretical guarantees for the consistency of RaFFLE under weak conditions, and its faster convergence when the data are generated by a linear model. Empirical evaluations on 136 regression datasets demonstrate that RaFFLE outperforms the classical CART and random forest methods, the regularized linear methods Lasso and Ridge, and the state-of-the-art XGBoost algorithm, across both linear and nonlinear datasets. By balancing predictive accuracy and computational efficiency, RaFFLE proves to be a versatile tool for tackling a wide variety of regression problems.
VideoDiff: Human-AI Video Co-Creation with Alternatives
Venue: arXiv cs.HC 2025
Year: 2025

To make an engaging video, people sequence interesting moments and add visuals such as B-rolls or text. While video editing requires time and effort, AI has recently shown strong potential to make editing easier through suggestions and automation. A key strength of generative models is their ability to quickly generate multiple variations, but when provided with many alternatives, creators struggle to compare them to find the best fit. We propose VideoDiff, an AI video editing tool designed for editing with alternatives. With VideoDiff, creators can generate and review multiple AI recommendations for each editing process: creating a rough cut, inserting B-rolls, and adding text effects. VideoDiff simplifies comparisons by aligning videos and highlighting differences through timelines, transcripts, and video previews. Creators have the flexibility to regenerate and refine AI suggestions as they compare alternatives. Our study participants (N=12) could easily compare and customize alternatives, creating more satisfying results.
A Multi-Simulation Approach with Model Predictive Control for Anafi Drones
Venue: arXiv cs.RO 2025
Year: 2025

Simulation frameworks are essential for the safe development of robotic applications. However, different components of a robotic system are often best simulated in different environments, making full integration challenging. This is particularly true for partially-open or closed-source simulators, which commonly suffer from two limitations: (i) lack of runtime control over scene actors via interfaces like ROS, and (ii) restricted access to real-time state data (e.g., pose, velocity) of scene objects. In the first part of this work, we address these issues by integrating aerial drones simulated in Parrot's Sphinx environment (used for Anafi drones) into the Gazebo simulator. Our approach uses a mirrored drone instance embedded within Gazebo environments to bridge the two simulators. One key application is aerial target tracking, a common task in multi-robot systems. However, Parrot's default PID-based controller lacks the agility needed for tracking fast-moving targets. To overcome this, in the second part of this work we develop a model predictive controller (MPC) that leverages cumulative error states to improve tracking accuracy. Our MPC significantly outperforms the built-in PID controller in dynamic scenarios, increasing the effectiveness of the overall system. We validate our integrated framework by incorporating the Anafi drone into an existing Gazebo-based airship simulation and rigorously test the MPC against a custom PID baseline in both simulated and real-world experiments.
Understanding the relationships between the perceptions of burnout and instability in Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Changes are inherent in software development, often increasing developers' perception of instability. Understanding the relationship between human factors and Software Engineering processes is crucial to mitigating and preventing issues. One such factor is burnout, a recognized disease that impacts productivity, turnover, and, most importantly, developers' well-being. Investigating the link between instability and burnout can help organizations implement strategies to improve developers' work conditions and performance. This study aims to identify and describe the relationship between perceived instability and burnout among software developers. A cross-sectional survey was conducted with 411 respondents, using convenience sampling and self-selection. In addition to analyzing variable relationships, confirmatory factor analysis was applied. Key findings include: (1) A significant positive relationship between burnout (exhaustion and cynicism) and team, technological, and task instability; (2) A weak negative relationship between efficacy and technological/team instability, with no correlation to task instability; (3) Exhaustion was the most frequently reported burnout symptom, while task instability was the most perceived type of instability. These results are valuable for both industry and academia, providing insights to reduce burnout and instability among software engineers. Future research can further explore the impact of instability, offering new perspectives on monitoring and mitigating its effects in software development.
Seamless acceleration of Fortran intrinsics via AMD AI engines
Venue: arXiv cs.DC 2025
Year: 2025

A major challenge that the HPC community faces is how to continue delivering the performance demanded by scientific programmers, whilst meeting an increased emphasis on sustainable operations. Specialised architectures, such as FPGAs and AMD's AI Engines (AIEs), have been demonstrated to provide significant energy efficiency advantages, however a major challenge is that to most effectively program these architectures requires significant expertise and investment of time which is a major blocker. Fortran in the lingua franca of scientific computing, and in this paper we explore automatically accelerating Fortran intrinsics via the AIEs in AMD's Ryzen AI CPU. Leveraging the open source Flang compiler and MLIR ecosystem, we describe an approach that lowers the MLIR linear algebra dialect to AMD's AIE dialects, and demonstrate that for suitable workloads the AIEs can provide significant performance advantages over the CPU without any code modifications required by the programmer.
MITO: A Millimeter-Wave Dataset and Simulator for Non-Line-of-Sight Perception
Venue: arXiv cs.CV 2025
Year: 2025

The ability to observe the world is fundamental to reasoning and making informed decisions on how to interact with the environment. However, optical perception can often be disrupted due to common occurrences, such as occlusions, which can pose challenges to existing vision systems. We present MITO, the first millimeter-wave (mmWave) dataset of diverse, everyday objects, collected using a UR5 robotic arm with two mmWave radars operating at different frequencies and an RGB-D camera. Unlike visible light, mmWave signals can penetrate common occlusions (e.g., cardboard boxes, fabric, plastic) but each mmWave frame has much lower resolution than typical cameras. To capture higher-resolution mmWave images, we leverage the robot's mobility and fuse frames over the synthesized aperture. MITO captures over 24 million mmWave frames and uses them to generate 550 high-resolution mmWave (synthetic aperture) images in line-of-sight and non-light-of-sight (NLOS), as well as RGB-D images, segmentation masks, and raw mmWave signals, taken from 76 different objects. We develop an open-source simulation tool that can be used to generate synthetic mmWave images for any 3D triangle mesh. Finally, we demonstrate the utility of our dataset and simulator for enabling broader NLOS perception by developing benchmarks for NLOS segmentation and classification.
A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems
Venue: arXiv cs.IR 2025
Year: 2025

Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With strict latency requirements, pre-ranking utilizes lightweight models to perform a preliminary selection from massive retrieved candidates. However, recent works focus solely on improving consistency with ranking, relying exclusively on downstream stages. Since downstream input is derived from the pre-ranking output, they will exacerbate the sample selection bias (SSB) issue and Matthew effect, leading to sub-optimal results. To address the limitation, we propose a novel Hybrid Cross-Stage Coordination Pre-ranking model (HCCP) to integrate information from upstream (retrieval) and downstream (ranking, re-ranking) stages. Specifically, cross-stage coordination refers to the pre-ranking's adaptability to the entire stream and the role of serving as a more effective bridge between upstream and downstream. HCCP consists of Hybrid Sample Construction and Hybrid Objective Optimization. Hybrid sample construction captures multi-level unexposed data from the entire stream and rearranges them to become the optimal guiding "ground truth" for pre-ranking learning. Hybrid objective optimization contains the joint optimization of consistency and long-tail precision through our proposed Margin InfoNCE loss. It is specifically designed to learn from such hybrid unexposed samples, improving the overall performance and mitigating the SSB issue. The appendix describes a proof of the efficacy of the proposed loss in selecting potential positives. Extensive offline and online experiments indicate that HCCP outperforms SOTA methods by improving cross-stage coordination. It contributes up to 14.9% UCVR and 1.3% UCTR in the JD E-commerce recommendation system. Concerning code privacy, we provide a pseudocode for reference.
SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel approach leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by about 70%, enabling effective performance at new locations without relying on any historical data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. SPIRIT represents a pivotal step towards rapid, scalable, and adaptable solar forecasting solutions, advancing the integration of renewable energy into global power systems.
Dynamic Fraud Proof
Venue: arXiv cs.CR 2025
Year: 2025

In this paper, we present a novel fraud-proof mechanism that achieves fast finality and, when combined with optimistic execution, enables real-time transaction processing. State-of-the-art optimistic rollups typically adopt a 7-day challenge window, during which any honest party can raise a challenge in case of fraud. We propose a new assert/challenge construction called "Dynamic Fraud Proofs" that achieves sub-second finality in ideal scenarios, while dynamically delaying settlement in the event of fraud detection and challenge resolution. The system relies on 1) a dynamic challenge period and 2) a configurable number of randomly selected verifier nodes who must interactively approve a state commitment without raising a challenge. If these conditions are not met, the state is not finalized, and the challenge period and approval criteria are dynamically adjusted. We provide a detailed analysis of the system's design, explaining how it maintains the assumption of a single honest node and addresses censorship attacks by inverting the traditional challenge process. Additionally, we formalize the system's probabilistic security model and discuss how bonding, incentives, and slashing mechanisms can encourage honest behavior, thereby increasing the likelihood of fast settlement in ideal scenarios.
TSP3D: Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we propose an efficient multi-level convolution architecture for 3D visual grounding. Conventional methods are difficult to meet the requirements of real-time inference due to the two-stage or point-based architecture. Inspired by the success of multi-level fully sparse convolutional architecture in 3D object detection, we aim to build a new 3D visual grounding framework following this technical route. However, as in 3D visual grounding task the 3D scene representation should be deeply interacted with text features, sparse convolution-based architecture is inefficient for this interaction due to the large amount of voxel features. To this end, we propose text-guided pruning (TGP) and completion-based addition (CBA) to deeply fuse 3D scene representation and text features in an efficient way by gradual region pruning and target completion. Specifically, TGP iteratively sparsifies the 3D scene representation and thus efficiently interacts the voxel features with text features by cross-attention. To mitigate the affect of pruning on delicate geometric information, CBA adaptively fixes the over-pruned region by voxel completion with negligible computational overhead. Compared with previous single-stage methods, our method achieves top inference speed and surpasses previous fastest method by 100\% FPS. Our method also achieves state-of-the-art accuracy even compared with two-stage methods, with $+1.13$ lead of Acc@0.5 on ScanRefer, and $+2.6$ and $+3.2$ leads on NR3D and SR3D respectively. The code is available at \href{https://github.com/GWxuan/TSP3D}{https://github.com/GWxuan/TSP3D}.
A Coordination-based Approach for Focused Learning in Knowledge-Based Systems
Venue: arXiv cs.AI 2025
Year: 2025

Recent progress in Learning by Reading and Machine Reading systems has significantly increased the capacity of knowledge-based systems to learn new facts. In this work, we discuss the problem of selecting a set of learning requests for these knowledge-based systems which would lead to maximum Q/A performance. To understand the dynamics of this problem, we simulate the properties of a learning strategy, which sends learning requests to an external knowledge source. We show that choosing an optimal set of facts for these learning systems is similar to a coordination game, and use reinforcement learning to solve this problem. Experiments show that such an approach can significantly improve Q/A performance.
Data Stewardship Decoded: Mapping Its Diverse Manifestations and Emerging Relevance at a time of AI
Venue: arXiv cs.CY 2025
Year: 2025

Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI). Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application. This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape. These manifestations include a) data stewardship as a set of competencies and skills, b) a function or role within organizations, c) an intermediary organization facilitating collaborations, and d) a set of guiding principles. The paper subsequently outlines the core competencies required for effective data stewardship, explains the distinction between data stewards and Chief Data Officers (CDOs), and details the intermediary role of stewards in bridging gaps between data holders and external stakeholders. It also explores key principles aligned with the FAIR framework (Findable, Accessible, Interoperable, Reusable) and introduces the emerging principle of AI readiness to ensure data meets the ethical and technical requirements of AI systems. The paper emphasizes the importance of data stewardship in enhancing data collaboration, fostering public value, and managing data reuse responsibly, particularly in the era of AI. It concludes by identifying challenges and opportunities for advancing data stewardship, including the need for standardized definitions, capacity building efforts, and the creation of a professional association for data stewardship.
You Can't Get There From Here: Redefining Information Science to address our sociotechnical futures
Venue: arXiv cs.CY 2025
Year: 2025

Current definitions of Information Science are inadequate to comprehensively describe the nature of its field of study and for addressing the problems that are arising from intelligent technologies. The ubiquitous rise of artificial intelligence applications and their impact on society demands the field of Information Science acknowledge the sociotechnical nature of these technologies. Previous definitions of Information Science over the last six decades have inadequately addressed the environmental, human, and social aspects of these technologies. This perspective piece advocates for an expanded definition of Information Science that fully includes the sociotechnical impacts information has on the conduct of research in this field. Proposing an expanded definition of Information Science that includes the sociotechnical aspects of this field should stimulate both conversation and widen the interdisciplinary lens necessary to address how intelligent technologies may be incorporated into society and our lives more fairly.
FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers
Venue: arXiv cs.CY 2025
Year: 2025

Different from traditional Business-to-Consumer e-commerce platforms~(e.g., Amazon), online fleamarket platforms~(e.g., Craigslist) mainly focus on individual sellers who are lack of time investment and business proficiency. Individual sellers often struggle with the bargaining process and thus the deal is unaccomplished. Recent advancements in Large Language Models(LLMs) demonstrate huge potential in various dialogue tasks, but those tasks are mainly in the form of passively following user's instruction. Bargaining, as a form of proactive dialogue task, represents a distinct art of dialogue considering the dynamism of environment and uncertainty of adversary strategies. In this paper, we propose an LLM-empowered bargaining agent designed for online fleamarket platform sellers, named as FishBargain. Specifically, FishBargain understands the chat context and product information, chooses both action and language skill considering possible adversary actions and generates utterances. FishBargain has been tested by thousands of individual sellers on one of the largest online fleamarket platforms~(Xianyu) in China. Both qualitative and quantitative experiments demonstrate that FishBargain can effectively help sellers make more deals.
Identifying relevant indicators for monitoring a National Artificial Intelligence Strategy
Venue: arXiv cs.CY 2025
Year: 2025

How can a National Artificial Intelligence Strategy be effectively monitored? To address this question, we propose a methodology consisting of two key components. First, it involves identifying relevant indicators within national AI strategies. Second, it assesses the alignment between these indicators and the strategic actions of a specific government's AI strategy, allowing for a critical evaluation of its monitoring measures. Moreover, identifying these indicators helps assess the overall quality of the strategy's structure. A lack of alignment between strategic actions and the identified indicators may reveal gaps or blind spots in the strategy. This methodology is demonstrated using the Brazilian AI strategy as a case study.
DRiVE: Dynamic Recognition in VEhicles using snnTorch
Venue: arXiv cs.NE 2025
Year: 2025

Spiking Neural Networks (SNNs) mimic biological brain activity, processing data efficiently through an event-driven design, wherein the neurons activate only when inputs exceed specific thresholds. Their ability to track voltage changes over time via membrane potential dynamics helps retain temporal information. This study combines SNNs with PyTorch's adaptable framework, snnTorch, to test their potential for image-based tasks. We introduce DRiVE, a vehicle detection model that uses spiking neuron dynamics to classify images, achieving 94.8% accuracy and a near-perfect 0.99 AUC score. These results highlight DRiVE's ability to distinguish vehicle classes effectively, challenging the notion that SNNs are limited to temporal data. As interest grows in energy-efficient neural models, DRiVE's success emphasizes the need to refine SNN optimization for visual tasks. This work encourages broader exploration of SNNs in scenarios where conventional networks struggle, particularly for real-world applications requiring both precision and efficiency.
MERGE$^3$: Efficient Evolutionary Merging on Consumer-grade GPUs
Venue: arXiv cs.NE 2025
Year: 2025

Evolutionary model merging enables the creation of high-performing multi-task models but remains computationally prohibitive for consumer hardware. We introduce MERGE$^3$, an efficient framework that makes evolutionary merging feasible on a single GPU by reducing fitness computation costs 50$\times$ while preserving performance. MERGE$^3$ achieves this by Extracting a reduced dataset for evaluation, Estimating model abilities using Item Response Theory (IRT), and Evolving optimal merges via IRT-based performance estimators. Our method enables state-of-the-art multilingual and cross-lingual merging, transferring knowledge across languages with significantly lower computational overhead. We provide theoretical guarantees and an open-source library, democratizing high-quality model merging.